フロアプランを事前情報として用いた長期屋内自己位置推定のためのメトリック・セマンティックマップ構築
Constructing Metric-Semantic Maps using Floor Plan Priors for Long-Term Indoor Localization
単眼RGB画像からの3D物体検出を活用し、フロアプランを事前情報としてメトリック・セマンティックマップを構築し、9ヶ月にわたる長期変化に頑健な物体ベースの自己位置推定を実現した。
著者: Nicky Zimmerman, Matteo Sodano, Elias Marks, Jens Behley, Cyrill Stachniss
分類: cs.RO, cs.CV
原文アブストラクト
Object-based maps are relevant for scene understanding since they integrate geometric and semantic information of the environment, allowing autonomous robots to robustly localize and interact with on objects. In this paper, we address the task of constructing a metric-semantic map for the purpose of long-term object-based localization. We exploit 3D object detections from monocular RGB frames for both, the object-based map construction, and for globally localizing in the constructed map. To tailor the approach to a target environment, we propose an efficient way of generating 3D annotations to finetune the 3D object detection model. We evaluate our map construction in an office building, and test our long-term localization approach on challenging sequences recorded in the same environment over nine months. The experiments suggest that our approach is suitable for constructing metric-semantic maps, and that our localization approach is robust to long-term changes. Both, the mapping algorithm and the localization pipeline can run online on an onboard computer. We release an open-source C++/ROS implementation of our approach.
